Mini-tablets (MTs) have been utilized as an alternative to monolithic tablets due to their ease of use for pediatric populations, dose flexibility and tailoring of drug release profiles. Similar to monolithic tablets, MTs can develop film coat and internal core defects during manufacturing processes that may adversely affect their dissolution performance. The use of x-ray computed microtomography (XRCT) is well documented for monolithic tablets as a means of identifying internal defects, but applications to MTs have not been well studied. In this study, we have developed a workflow that analyzes reconstructed XRCT images of enteric-coated mini-tablets using deep learning convolutional neural networks. This algorithm was utilized to extract key physical features of individual MTs, such as micro-crack volume and enteric coat thickness. By performing dissolution studies on individual MTs, correlations were established based on the physical parameters obtained by XRCT and the dissolution performance, enabling prediction of dissolution performance utilizing non-destructive imaging data. This workflow provides insight into the physical variability of MT populations that are generated during manufacturing, enabling optimization of critical tableting and coating parameters to achieve the target dissolution criteria. Through this mechanistic understanding, quality is built into the final drug product through rational development of formulation and process parameters.
Keywords: X-ray computed microtomography; convolutional neural network image segmentation; mini-tablets; oral solid dosages; quality by design; tablet dissolution.
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